Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists.

Background: Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including in Otolaryngology-Head & Neck Surgery. In this study, we investigated whether such CDST may influence otolaryngologists' diagnostic judgement.

Methods: Otolaryngologists were recruited virtually across the United States for this experiment on human-AI interaction.

The use of thyroid isthmusectomy for management of well differentiated thyroid carcinoma - A systematic review and meta-analysis.

Objective: With the growing global incidence of thyroid carcinomas, there is an increasing need for distinct guidelines for isthmus-confined carcinomas. Here, we performed the first systematic review on the topic to date, aiming to provide understanding to isthmusectomy as surgical management for well-differentiated thyroid carcinoma of the isthmus.

Methods: We conducted a systematic review following the PRISMA guidelines, analyzing English-language studies from the past decade that report on thyroid isthmusectomy.

A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy.

Purpose: To develop and validate a deep learning model for distinguishing healthy vocal folds (HVF) and vocal fold polyps (VFP) on laryngoscopy videos, while demonstrating the ability of a previously developed informative frame classifier in facilitating deep learning development.

Methods: Following retrospective extraction of image frames from 52 HVF and 77 unilateral VFP videos, two researchers manually labeled each frame as informative or uninformative. A previously developed informative frame classifier was used to extract informative frames from the same video set.

Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients.

Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma.

Methods And Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression.

Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Objective: Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning models for automatic segmentation of postoperative vestibular schwannomas on gadolinium-enhanced T1-weighted magnetic resonance imaging (GdT1WI) and noncontrast high-resolution T2-weighted magnetic resonance imaging (HRT2WI).

Study Design: A supervised machine learning approach using a U-Net model was applied to segment magnetic resonance imaging images into pixels representing vestibular schwannoma and background pixels.

A scoping review of the unassisted physical exam conducted over synchronous audio-video telemedicine.

Background: This scoping review aims to provide a broad overview of the research on the unassisted virtual physical exam performed over synchronous audio-video telemedicine to identify gaps in knowledge and guide future research.

Methods: Searches for studies on the unassisted virtual physical exam were conducted in 3 databases. We included primary research studies in English on the virtual physical exam conducted via patient-to-provider synchronous, audio-video telemedicine in the absence of assistive technology or personnel.

Automatic classification of informative laryngoscopic images using deep learning.

Objective: This study aims to develop and validate a convolutional neural network (CNN)-based algorithm for automatic selection of informative frames in flexible laryngoscopic videos. The classifier has the potential to aid in the development of computer-aided diagnosis systems and reduce data processing time for clinician-computer scientist teams.

Methods: A dataset of 22,132 laryngoscopic frames was extracted from 137 flexible laryngostroboscopic videos from 115 patients.

Times to Diagnosis, Staging, and Treatment of Head and Neck Cancer Before and During COVID-19.

Objective: The coronavirus disease 2019 (COVID-19) pandemic has reduced the demand for, and supply of, head and neck cancer services. This study compares the times to diagnosis, staging, and treatment of head and neck cancers before and during the COVID-19 pandemic.

Study Design: Retrospective cohort study.

Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review.

Objectives/hypothesis: This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research.

Study Design: Scoping Review.

Methods: Searches for studies on AI and office laryngoscopy were conducted in five databases.

Differences in antibiotic prescriptions between direct-to-consumer telehealth and telehealth in the emergency department.

Background: Telemedicine, which allows physicians to assess and treat patients via real-time audiovisual conferencing, is a rapidly growing modality for providing medical care. Antibiotic stewardship is one important measure of care quality, and research on antibiotic prescribing for acute respiratory infections in direct-to-consumer telemedicine has yielded mixed results. We compared antibiotic prescription rates for acute respiratory infections in two groups treated by telemedicine: (1) patients treated via a direct-to-consumer telemedicine application and (2) patients treated via telemedicine while physically inside the emergency department.

Micro Satellite Orbital Boost by Electrodynamic Tethers.

In this manuscript, a method for maneuvering a spacecraft using electrically charged tethers is explored. The spacecraft's velocity vector can be modified by interacting with Earth's magnetic field. Through this method, a spacecraft can maintain an orbit indefinitely by reboosting without the constraint of limited propellant.

Antibiotic Prescribing Practices: Is There a Difference Between Patients Seen by Telemedicine Versus Those Seen In-Person?

Direct-to-consumer telemedicine is becoming part of mainstream medicine, but questions exist regarding the quality of care provided. We assessed antibiotic stewardship, one measure of quality, by comparing antibiotic prescription rates for acute respiratory infections (ARIs) between patients seen by telemedicine and patients seen in-person in two urban emergency departments (EDs). In two urban EDs where low-acuity patients in the ED have the option of being seen by telemedicine rather than in-person, we analyzed telemedicine and in-person visits of patients ≥18 years who received ARI diagnoses between July 2016 and September 2017.

On the mechanism of Lewis base catalyzed aldol addition reactions: kinetic and spectroscopic investigations using rapid-injection NMR.

The mechanistic foundations of the Lewis base catalyzed aldol addition reactions have been investigated. From a combination of low-temperature spectroscopic studies ((29)Si and (31)P NMR) and kinetic analyses using a rapid-injection NMR apparatus (RINMR), a correlation of the ground states and transition structures for the aldolization reactions has been formulated. The aldol addition of the tert-butylsilyl ketene acetal of tert-butyl propanoate with 1-naphthaldehyde is efficiently catalyzed by a combination of silicon tetrachloride and chiral phosphoramide Lewis bases.